Adding　to　societal　changes　today,　are　the　miscellaneous　big　data　produced　in　different　fields.　Coupled　with　these　data　is　the　appearance　of　risk　management.　Admittedly,　to　predict　future　trend　by　using　these　data　is　conducive　to　make　everything　more　efficient　and　easy.　Now,　no　matter　companies　or　individuals,　they　increasingly　focus　on　identifying　risks　and　managing　them　before　risks.　Effective　risk　management　will　lead　them　to　deal　with　potential　problems.　This　thesis　focuses　on　risk　management　of　flight　delay　area　using　big　real　time　data.　It　proposes　two　different　prediction　models,　one　is　called　General　Long　Term　Departure　Prediction　Model　and　the　other　is　named　as　Improved　Real　Time　Arrival　Prediction　Model.　By　studying　the　main　factors　lead　to　flight　delay,　this　thesis　takes　weather,　carrier,　National　Aviation　System,　security　and　previous　late　aircraft　as　analysis　factors.　By　utilizing　our　models　can　do　not　only　long　time　but　also　short　term　flight　delay　predictions.　The　results　demonstrate　goodness　of　fit.　Besides　the　theory　part,　it　also　presents　a　practical　and　beautiful　web　application　for　real　time　flight　arrival　prediction　based　on　our　second　model.　©　2015　IEEE.